Dienstag, 26.09.2017 03:56 Uhr

Machine learning

Verantwortlicher Autor: Carlo Marino Rome, 08.09.2017, 10:52 Uhr
Kommentar: +++ Internet und Technik +++ Bericht 3248x gelesen

Rome [ENA] Machine learning is already a part of our everyday lives and just one of many examples is that Amazon is opening a new research and development center in Turin (Italy) aiming at developing the artificial intelligence of its Echo voice-enabled wireless speaker, which responds to the name Alexa. There is no doubt that Artificial Intelligence and Machine learning are this century's major technological advance.

Machine learning is an area of AI (artificial intelligence) that lets computer systems to learn directly from examples, data and experience empowering them to perform specific tasks intelligently. Consequently, machine learning systems can carry out complex processes by learning. Instead of following pre-programmed rules to perform a task, machine learning allows a system to absorb how to perform that task. This technology is developing at a fast pace, due to the increasing availability of data, amplified computing power to analyze this data, and technical progresses in the field.

Machine learning can drive advances in healthcare, teaching, transport, and over, backing better public services and boosting the economy. There is the opportunity now, as a society, to ensure that machine learning can bring the maximum benefit to the largest number of people.What is the potential of machine learning over the next 5-10 years? And how it’s possible to develop this technology in a way that benefits everyone?

Recent years have seen stimulating advances in machine learning, which have raised its capabilities across a suite of applications. Many people now interact with systems based on machine learning every day, for example in image recognition systems, such as those used on social media; voice recognition systems, used by virtual personal assistants; and recommender systems, for instance those used by online retailers. As the field advances further, machine learning has the possibilities to support potentially transformative advances in a wide range of fields, and the social and economic opportunities which go along are significant.

As machine learning is put to use with an increasing breadth of applications, it generates new questions or debates, which in succession open up further opportunities. New technologies and discovery are key elements for modern societies. Legislators also are obliged to guarantee that the benefits arising are adequately mirrored within the community, that information is put to use and that innovation is completely exploited.Public dialogue shows that people welcome many of the benefits that machine learning has already produced and are tremendously interested in its wide-ranging potential. As with most new technologies, the public also have concerns that need to be addressed.

Machine learning has the potential to be a disruptive technology, and it could play a central role in helping to cope with the productivity gap. There’s such an information overload that it’s becoming hard for even the smartest humans to master it in their lifetimes. How do human beings search through this inundation of data to find the right intuitions and turn that into knowledge? Machine learning and AI can be useful to tackle absolutely complex problems in areas like cancer, climate change, energy, genomics, macroeconomics, financial systems, robotics, and physics.

Advancements in medical diagnoses, the development of new treatments, efficient energy systems, and driverless cars all implicate uses of data, including personal information, which may become sensitive. It’s crucial to examine new uses of data and their implications and make proposals about the future data governance landscape.Like other major new technologies, machine learning will affect many types of work. It can perform well at specific tasks and in many cases this can be used to support human roles. The nature and extent of the impact is not clear, but is already being broadly discussed. Its implications for education and skills raise questions about how best to distribute the benefits, among other things.

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